OVERWEIGHT AND OBESITY IN CHILDREN AND ADOLESCENTS: ETIOLOGY, COMPLICATIONS AND EFFECTS OF 12-MONTHS INTERVENTION

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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES MEDICAL ACADEMY

Natalija Smetanina

OVERWEIGHT AND OBESITY IN

CHILDREN AND ADOLESCENTS:

ETIOLOGY, COMPLICATIONS

AND EFFECTS OF 12-MONTHS

INTERVENTION

Doctoral Dissertation Biomedical Sciences, Medicine (06B) Kaunas 2016 1

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Dissertation has been prepared at the Department of Endocrinology of Medical Academy of Lithuanian University of Health Sciences during the period of 2008–2016.

Scientific Supervisor:

Prof. Dr. Rasa Verkauskienė (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B)

Consultant:

Prof. Dr. Habil. Apolinaras Zaborskis (Lithuanian University of Health Sciences, Biomedical Sciences, Public Health – 09B)

Dissertation is defended at the Medical Research Council of Lithuanian University of Health Sciences.

Chairperson

Assoc. Prof. Dr. Džilda Veličkienė (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B)

Members:

Prof. Dr. Habil. Limas Kupčinskas (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B)

Prof. Dr. Habil. Daiva Rastenytė (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B)

Dr. Aivaras Ratkevičius (Lithuanian Sports University, Biomedical Sciences, biology – 01B)

Prof. Dr. Martin Wabitsch (University of Ulm (Germany), Biomedical Sciences, Medicine – 06B)

Dissertation will be defended at the open session of the Medical Research Council at 2 p.m. on the 30th of August, 2016 in the Grand Auditorium of the Department of Endocrinology of Lithuanian University of Health Sciences.

Address: Eivenių 2, LT-50009 Kaunas, Lithuania. 2

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LIETUVOS SVEIKATOS MOKSLŲ UNIVERSITETAS MEDICINOS AKADEMIJA

Natalija Smetanina

VAIKŲ IR PAAUGLIŲ ANTSVORIS

IR NUTUKIMAS: ETIOLOGIJA,

PASEKMĖS IR 12 MĖNESIŲ

INTERVENCIJOS POVEIKIS

Daktaro disertacija Biomedicinos mokslai, medicina (06B) Kaunas 2016 3

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Disertacija rengta 2008–2016 metais Lietuvos sveikatos mokslų universitete Medicinos akademijos Endokrinologijos klinikoje.

Mokslinis vadovas

Prof. dr. Rasa Verkauskienė (Lietuvos sveikatos mokslų universitetas, biomedicinos mokslai, medicina – 06B)

Konsultantas

Prof. habil. dr. Apolinaras Zaborskis (Lietuvos sveikatos mokslų uni-versitetas, biomedicinos mokslai, visuomenės sveikata – 09B)

Disertacija ginama Lietuvos sveikatos mokslų universiteto Medicinos mokslo krypties taryboje:

Pirmininkė

Doc. dr. Džilda Veličkienė (Lietuvos sveikatos mokslų universitetas, biomedicinos mokslai, medicina – 06B)

Nariai:

Prof. habil. dr. Limas Kupčinskas (Lietuvos sveikatos mokslų universi-tetas, biomedicinos mokslai, medicina – 06B)

Prof. habil. dr. Daiva Rastenytė (Lietuvos sveikatos mokslų universi-tetas, biomedicinos mokslai, medicina – 06B)

Dr. Aivaras Ratkevičius (Lietuvos sporto universitetas, biomedicinos mokslai, biologija – 01B)

Prof. dr. Martin Wabitsch (Ulmo universitetas (Vokietija), biomedici-nos mokslai, medicina – 06B)

Disertacija ginama viešame Medicinos mokslo krypties tarybos posėdyje 2016 m. rugpjūčio 30 d. 14 val. Lietuvos sveikatos mokslų universiteto Endokrinologijos klinikoje Didžiojoje auditorijoje.

Adresas: Eivenių g. 2, LT-50009, Kaunas, Lietuva. 4

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ABBREVIATIONS

12 mo F/U – 12 months follow-up 5-mC – 5-methylcytosine AH – arterial hypertension

IIH – idiopathic intracranial hypertension ALT – alanine aminotransferase

AST – aspartate aminotransferase B/I – before intervention BMD – bone mineral density BMI – body mass index BP – blood pressure

CDC – Centers for Disease Control CI – confidence interval

COSI – Childhood Obesity Surveillance Initiative CRP – C-reactive protein

CVD – cardiovascular diseases

DHEAS – dehydroepiandrosterone sulphate DNMT – DNA methyltransferase

E2 – estradiol

ESPGHAN – European Society for Pediatric Gastroenterology, Hepatology and Nutrition

FAI – free androgen index

FFQ – food frequency questionnaire FPG – fasting plasma glucose FSH – follicle stimulating hormone FT3 – free triiodthyronine

FT4 – free thyroxine

HDL – high-density lipoprotein cholesterol

HOMA-B – homeostasis model assessment of β-cell function HOMA-IR – homeostasis model assessment of insulin resistance hs-CRP – high sensitivity CRP

I/G_30 – insulin-to-glucose ratio at 30 min of OGTT IGM – impaired glucose metabolism

IMT – intima-media thickness

IOTF – International Obesity Task Force IR – insulin resistance

IRMA – immunoradiometric assay IS – insulin sensitivity

ISICederholm – Cederholm peripheral insulin sensitivity index

ISIGutt – Gutt insulin sensitivity index

ISIMatsuda – Matsuda whole body insulin sensitivity index

GH – growth hormone LBM – lean body mass LH – luteinizing hormone

LDL – low density lipoprotein cholesterol MRI – magnetic resonance imaging MS – metabolic syndrome

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mtDNA – mitochondrial DNA

NAFLD – nonalcoholic fatty liver disease NASH – nonalcoholic steatohepatitis

NHLBI – National Heart, Lung and Blood Institute NS – non-significant

NW – normal weight OB – obesity

Ob – obese

OGTT – oral glucose tolerance test OSA – obstructive sleep apnea OW – overweight

PCOM – polycystic ovary morphology PCOS – polycystic ovarian syndrome PiA – physical inactivity

PNFI – pediatric NAFLD fibrosis index PNHS – Pediatric NAFLD Histological Score PPARγ – peroxisome proliferator activated receptor γ

PPARGC1A – peroxisome proliferator-activated receptor gamma coactivator 1 alpha QUICKI – quantitative insulin sensitivity check index

QoL – quality of life RIA – radioimmune assay SD – standard deviation SDS – standard deviation score SES – socioeconomic status SFT – skinfold thickness

SHBG – sex hormone binding globulin T2D – diabetes type 2

TBF – total body fat

TBFGoran – total body fat by Goran

TBFSlaughter – total body fat by Slaughter

TCh – total cholesterol TG – triglycerides

TSH – thyroid stimulating hormone TV – television

US – United States UW – underweight WC – waist circumference

WGOC – Working Group on Obesity in China WHO – World Health Organisation

WHpR – waist-to-hip ratio WHtR – waist-to-height ratio

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INTRODUCTION

The prevalence of overweight (OW) an obesity (OB) has increased among children and adolescents also in adults in many countries during the last two decades [237]. There is a considerable public health concern about future risk for obesity related diseases. Recent studies showed that child-hood OW/OB increases the risk of metabolic complications such as insulin resistance (IR), type 2 diabetes (T2D), metabolic syndrome (MS), polycys-tic ovarian syndrome (PCOS) in adolescence and adulthood [39, 79, 275, 278]. Non-alcoholic fatty liver disease (NAFLD) recognition is based on detection of fatty liver combined with risk factors (mainly central OB/OW) and exclusion of other liver diseases [403]. Importantly, the degree of heap-tic enzymes elevation does not correlate with the presence or severity of histological findings of NAFLD [58, 294]. A number of children with normal or slightly elevated alanine aminotransferase (ALT) levels may have advanced fibrosis on liver biopsy [403]. There is a significant shortfall of data on childhood OB-related metabolic complications prevalence in Lithuania.Childhood OB and its complications have no generally accepted control and intervention guidelines. Use of psychological interventions such as cognitive behavioural therapy combined with strategies to improve diet and physical activity showed to be promising [187]. However, according to previous studies results, lifestyle and dietary changes are not always effective in controlling weight [249, 288]. Only in few studies obese (Ob) pediatric patients were treated with insulin sensitizers [140, 189, 384], and there is still lack of consensus on most effective OB treatment in children and adolescents.

Peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A) polymorphism is found to be associated with risk for OB and higher triglyceride (TG) levels [10, 134]. Recent study results showed that PPARGC1A upregulates transcription of genes involved in mitochondrial oxidative metabolism and glucose transport in skeletal muscles and is associated with IR [266, 322, 323, 435]. However, there are no established data how targeted intervention and weight changes influences the PPARGC1A methylation, gene activation and biochemical changes such as IR, lipid profile in Ob children and adolescents.

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1. AIM AND OBJECTIVES OF THE STUDY

Aim of the study

To evaluate the prevalence and etiology of overweight and obesity among 10–17 years old children and adolescents in Kaunas region, Lithuania, to assess obesity-related complications and to determine the impact of targeted intervention on weight control, metabolic status and changes in PPARGC1A gene methylation.

Objectives:

1) To identify the prevalence of overweight and obesity in 10–17 years old children and adolescents in Kaunas area, Lithuania;

2) To evaluate the influence of children/adolescents’ dietary habits, physical inactivity, and psychosocial factors, as well as parental education, social status, and family history of overweight, type 2 diabetes and arterial hypertension on body mass index in children and adolescents;

3) To evaluate obesity-related metabolic complications, such as metabolic syndrome, insulin resistance, impaired glucose metabo-lism, non-alcoholic fatty liver disease, and the risk for liver fibrosis in the studied cohort;

4) To assess polycystic ovarian syndrome in overweight and obese adolescent girls in relation with metabolic profile;

5) To investigate the influence of diet and lifestyle changes versus pharmaceutical intervention with insulin sensitizer Metformin on anthropometric parameters and obesity-related complications in overweight/obese children and adolescents;

6) To evaluate the association of targeted intervention and PPARGC1A gene methylation.

Hypothesis:

1. Dietary habits, physical activity, parental education, and family history of both obesity and type 2 diabetes influence the risk of overweight/obesity in children and adolescents;

2. Metabolic profile of overweight/obese children and adolescent is directly related on obesity level;

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3. Intervention with Metformin versus non-pharmaceutical interven-tion and controls is more effective in weight reducinterven-tion and meta-bolic profile improvement in overweight/obese children and ado-lescents.

4. 12 months intervention with Metformin reduces PPARGC1A methylation significantly more compared to lifestyle changes intervention.

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2. NOVELTY OF THE STUDY

The prevalence of OB among Lithuanian children and adolescents has lastly been studied in 2002, and there is a need for recent evaluation. Body mass index (BMI) is considered to be a surrogate marker of adiposity, and waist circumference (WC) reflects central OB, related to the risk of metabolic disorders and is used in the definition of MS in children and in adults. Only few countries have national references for OB evaluation in children and adolescents according to BMI, WC, waist-to-height ratio (WHtR) and waist-to-hip ratio (WHpR) by gender and age and there are still no available references of BMI and WC in pediatric population in Lithuania. Practical input of the epidemiological part of this study is to establish the prevalence of OW/OB in children and adolescents aged 10–17 years and to create national references for OB evaluation (BMI, WC, WHpR, and WHtR percentiles by gender and age).

There is a significant shortfall of data on childhood OB-related meta-bolic complications prevalence in Lithuania. Only few studies in adults sho-wed the association of MS with health concerns. A 10-year prospective study by Kazlauskiene et al. determined two times higher risk for myocar-dial infarction and stroke in individuals with MS [206]. Another study which followed-up a cohort for 35 years reported association of risks for MS, hyperglycemia, T2D, arterial hypertension (AH) and dyslipidemia with childhood BMI and it’s gain from childhood to adulthood [324]. The clinical part of our study highlights the prevalence and associations of OB-related complications in OW/Ob children and adolescents in Lithuania.

Pediatric NAFLD fibrosis index, a non-invasive index for the prediction of liver fibrosis in children with NAFLD, is obtained from simple measures and may be used instead of liver biopsy to diagnose liver fibrosis in children with NAFLD [403]. Use of this index in clinical practice may be helpful for liver fibrosis prediction in OW/Ob children and adolescents with NAFLD clinical features.

Several studies showed that adding physical activity to a dietary restric-tion intervenrestric-tion does not lead to addirestric-tional weight loss among Ob youth [151, 268]. Pharmacotherapy options for the treatment of pediatric OB are very limited. Recent studies showed positive impact of Metformin on weight and IR in insulin-resistant OW/Ob children and adolescents, but it is unclear if Metformin is effective for weight and OB-related complications reduction in OW/Ob children and adolescents with normal insulin sensiti-vity (IS). In the intervention part of our study we aimed to investigate the effects of targeted intervention with Metformin and lifestyle changes on

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weight control and reduction of OB-related complications, as well as on epigenetic changes in PPRGC1A gene, involved in the control of IS. To our knowledge, interventions effects on epigenetic changes of the PPRGC1A gene associated with IR have not been previously studied.

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3. REVIEW OF LITERATURE

3.1. Overweight/obesity assessment and definition in children and adolescents

World Health Organization (WHO) defines OB as an excess of body fat or adiposity great enough to increase the risk of morbidity, altered physical, physiological, or social well-being [1, 167]. Body weight itself can provide an identification of fat stores, but body composition is extremely variable. Other measurements, such as BMI, WC, WHpR, skinfold thickness (SFT), and bioimpedance better estimate body fat and quantify health risk [167, 388].

BMI is a main measure to determine childhood OW and OB. BMI is calculated by dividing a person's weight in kilograms by the square of height in meters. OW is defined as a BMI at or above the 85th percentile (or standard deviation (SD) +1.0) and below the 97th percentile (SD +2.0) for children and adolescents of the same age and sex. OB is defined as a BMI at or above the 97th percentile (SD +2.0) for children and adolescents of the same age and sex [87].

BMI may overestimate adiposity in a child with increased muscle mass, as may be the case in an athletic child and underestimate adiposity in a child with reduced muscle mass, such as sedentary child [167, 347]. Therefore, additional evaluation is valuable in athletes, who have increased body weight due to massive muscle mass [352].

Previous studies showed that WC has been shown to be a robust and valid index of central (abdominal) adiposity in children and is associated with MS components including IR [441].

WHpR is a good indicator for identifying children and adolescents at risk for developing high blood pressure (BP) [160, 439].

WHtR has predictive value to BMI for OB-related cardiometabolic complications not only among OW/Ob but also normal weight children and adolescents [270, 325].

SFT is not only a measure of subcutaneous fat deposits, but also has a strong positive relationship with increased BP, TG, low density lipoprotein cholesterol (LDL) reduced high-density lipoprotein cholesterol (HDL) and IR in youth; all factors markedly increasing risk for AH, MS, and CVD [2, 137, 139, 192, 389]. However, in Ob children SFT measurement has a significant error, often is hard to identify an appropriate skinfold to measure. SFT data are of greatest value if left in their raw state. In this forms they provide accurate evaluation of specific subcutaneous fat depots [335].

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Bioimpedance analysis is precise enough for total lean mass evaluation [247, 396].

The gold standard for measuring internal adiposity is magnetic reso-nance imaging (MRI), however, it has limited practical apply due to high cost.

The combination of raw SFT measurements and WC-SD score (-SDS) is helpful assessment of adiposity in individuals, at the same time all these techniques are convenient, cheap, portable, non-invasive an can be repeated for longitudinal follow-up [335].

Nowadays, several international references are available. The Inter-national Obesity Task Force (IOTF) references established for 2–18-year-olds have some advantages and disadvantages. They are internationally based, linked with mortality rates, but are less geographically and temporary dependent than some other cut-offs and provide limited centile ranges, also allow calculating SDS for every half year age period [89]. Moreover, IOTF cut-offs have been widely used in prevalence studies, thus making enable studies of time trends [347].

WHO standards and references also have some advantages. References data starts at birth and depict physiological growth. Created software converting to SDS various anthropometric measurements allows expressing measurements as continuous variables [347].

Some large countries use national BMI references, for example, Centers for Disease Control and Prevention (CDC) references are used in United States (US) for children and adolescents’ OW and OB evaluation [298]. This fact should be taking into account and compare data with other countries carefully.

3.2. Prevalence of overweight/obesity in childhood and adolescence

Last two decades public health researches pointed to rising prevalence of OW and OB as in children also among adults. The reviews of recent studies, however, showed an establishment and, in some countries, even a decrease in rates of OB among children and adolescents [286, 346].

Previous studies reported a large variation in the prevalence of child-hood OW and OB ranging from 2.9% to 44.4% across various countries [42, 55, 149, 211, 296, 355, 385, 394]. Comparison of prevalence data between OW/Ob children and adolescents among different countries is presented in Table 3.2.1.

Studies addressing the issue of OB among children and adolescents in Lithuania are scarce; to our knowledge, since 2002, no study investigating

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the prevalence of OW/OB among 10–17 years age children has been carried out in Lithuania WHO Childhood Obesity Surveillance Initiative (COSI) project data presented the level of OW among 6–9 year-old children [430].

Table 3.2.1. Prevalence of overweight and obesity in children and

ado-lescents, by age and gender

Country Investigator (publication date) Reference Gender (M – males, F – females) Age, years Over-weight, % Obe-sity, % Lithuania Tutkuviene et al., 2007 [400] IOTF M F M F 7–13 7–13 14–18 14–18 5.05–11.21 4.6–10.5 2.94–10.43 1.5–6.6 0–4.1# 0–2.9# 0–1.8# 0 Smetanina et al., 2015 [378] IOTF M M M F F F 7–17 10–13 14–17 7–17 10–13 14–17 12.6 11.6 12.8 12.6 14.9 6.8 4.9 4.6 2.4 3.4 3.1 1.8 Sweden:  Gothen-burg  Northern Sweden Neovius et al., 2006 [284] IOTF M+F M+F 10 10 18.0 30.0 2.9

Norway Brug et al., 2012 [55] IOTF M F 10–12 10–12 15.1 13.8 0.4 2.4 Denmark Matthiessen et al., 2008 [259] IOTF M+F 4–18 14.1 2.4 Schmidt Morgen, 2013 et al. [359] WHO M+F 11–16 9.9–18.5 1.9–4.4

Slovenia Brug et al., 2012 [55] IOTF M F 10–12 10–12 31.7 22.5 7.5 3.9 Italy Lazzeri et al.,

2008 [233] IOTF M+F M+F M+F 11 13 15 19.6 17.9 19.7 France Thibault et al.,

2012 [394] IOTF M+F 7–11 15.6 2.9 Keke et al., 2015 [209] IOTF WHO French references M+F M+F M+F 4–12 4–12 4–12 16.2 20.0 13.8 6.7 11.6 6.7 16

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Table 3.2.1. Continued Country Investigator (publication date) Reference Gender (M – males, F – females) Age, years Over-weight, % Obe-sity, %

Poland Bac et al., 2011 [27] IOTF M F 6–13 6–13 28.0 16.2 7.0 3.6 Austria Walner et al.,

2010 [418]

WHO M+F 18 15.7 5.4

Germany Kurth et al., 2007 [221] Kromeyer-Hauschild reference system M+F 14–17 17.0 8.5

Belgium Brug et al., 2012 [55] IOTF M F 10–12 10–12 16.9 13.5 3.7 2.3 Switzerland Stamm et al.,

2014 [386] IOTF M+F 5–14 17.0 3.9 Czech Republic Sigmundova et al., 2014 [370] WHO M M M F F F 11 13 15 11 13 15 30.7 27.3 22.3 15.7 11.0 11.4 Hungary Antal et al.,

2009 [22] IOTF M F 7–14 7–14 18.1 19.6 7.4 6.3 Brug et al., 2012 [55] IOTF M F 10–12 10–12 27.7 22.6 6.8 4.1 Spain Brug et al.,

2012 [55] IOTF M F 10–12 10–12 25.8 23.8 2.9 3.1 Valdes Pizzaro et al., 2012 [404] IOTF M+F 2–15 18.8 10.3 Garcia Garcia et al., 2013 [147] IOTF M+F 6–12 12–16 13.6 31.0 8.0 11.6 Greece Brug et al.,

2012 [55] IOTF M+F M F 9–13 10–12 10–12 30.5 44.4 37.7 11.6 11.2 9.7 Portugal Sardinha et al.,

2010 [355] IOTF WHO M F M F 10–18 10–18 10–18 10–18 17.7 17.0 20.4 23.1 5.8 4.6 10.3 9.6 17

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Table 3.2.1. Continued Country Investigator (publication date) Reference Gender (M – males, F – females) Age, years Over-weight, % Obe-sity, %

Netherlands Brug et al., 2012 [55]

IOTF M+F 10–12 16.8 4.5 Israel Nitzan Kaluski

et al., 2009 [289] Centers for Disease Control and Prevention (CDC) M+F 11–19 13.0–15.0 4.0–9.0

Cyprus Savva et al., 2014 [357]

IOTF M+F 10–18 20.1 8.1 Australia O’Dea et al.,

2011 [296]

IOTF M+F 6–18 18.3 6.6

Canada Shields et al., 2010 [367] IOTF WHO CDC M+F M+F M+F 2–17 2–17 2–17 26.0 35.0 28.0 8.0 13.0 13.0 USA Ogden et al.,

2006 [297] CDC M+F 6–11 12–19 18.8 17.4 Ogden et al, 2012 [299] CDC M+F 6–11 12–19 18.0 18.4 Ogden et al, 2014, [298] CDC M+F M F 6–11 12–19 6–11 12–19 6–11 12–19 34.2 34.5 33.2 35.1 35.2 33.8 17.7 20.5 16.9 20.3 19.1 20.7 Skinner et al., 2014 [377] CDC M+F M F 6–11 12–19 2–19 2–19 33.1 33.2 31.6 30.7 17.5 18.0 17.1 15.6 China Dong et al.,

2014 [116]

WGOC M+F 6–17 10.9 8.7

Iran Kelishadi et al., 2008 [210]

ND M+F 6–18 11.3 2.9

#, min-max; CDC, Centers for Disease Control and Prevention; IOTF, International Obesity Task Force; WGOC, Working Group on Obesity in China; ND, no data.

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3.3. Childhood obesity etiology

Increasing energy consumption, decreasing energy expenditure, or a combination of both has led to a positive energy balance and marked increase in weight in our society [388]. Pediatric OB is a multifactorial condition which is a result of genetic, metabolic, behavioral, and environ-mental influences and the complex interaction among these [167, 388]. Significant gender differences and considerable impact of familial socioeco-nomic background [242, 243, 419] highlighting the importance of familial psychosocial environment [125] for child’s OW development. Recent publi-cations highlighted the risk factors for children OW/OB development:

• Intrauterine exposure to maternal adiposity, diabetes and gestational diabetes: the offspring of such mothers have increased risk of childhood and elderly adult OB [71, 97, 167, 217].

• Birth weight: premature infants and small for gestational age babies who exhibit rapid catch-up growth have increased OB risk in later childhood [71, 167,303].

• Rapid weight gain: Rapid postnatal weight gain during the first 2 years is known to be positively associated with higher BMI and adiposity during the preschool years [300, 392]. The earlier a child becomes OW, and the longer excess weight is maintained, the greater the risk that the child’s OW will follow into adulthood [100]. Recent studies have shown that OB is less prominently associated with morbidity in adolescence but is a strong precursor for OB and related morbidity in adulthood, with 50% to 80% of Ob adolescents becoming Ob adults [39]. Longitudinal study results by Cuningham et al. found that the incidence of OB between the ages 5 and 14 years was 4 times as high among children who had been OW at the age of 5 years as among children who had a normal weight at that age [94].

• Breastfeeding: absence of breastfeeding is associated with higher BMI in later childhood [125].

• Dietary habits: inappropriate eating habits in the early years of life, consumption of large quantities of beverages rich in sugar, breakfast skipping, high-energy, high fat and low-fiber food were shown to be related with OW and OB in children and adolescents [17, 268]. Albertson with colleagues in their longitudinal study have found that regular cereal consumption through adolescence is associated with significantly lower percent body fat, lower total cholesterol (TCh), less television (TV) viewing, and higher rates of

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physical activity [9]. Seldom breakfast consumption increases the risk of MS significantly compared to regular breakfast [363]. As it has been shown in Nordic and Baltic countries study, educational level is positively associated with vegetable consumption in adults [331].

• Physical inactivity (PiA): low levels of physical activity, greater hours of TV/other screen time are associated with childhood OB risk [44, 55, 167]. Several studies reported that PiA is associated with increased BMI, weight gain, OW/OB in children and adults [92, 104, 179, 332, 380, 399]. Based on TV viewing and screen-time in children and adolescents, there is a strong evidence of a relationship between sedentary behavior and OB, also a moderate evidence for BP and TCh, self-esteem, social behavior problems physical fitness and academic achievement association with OB [104].

• Sleep duration: shorter sleep duration in infancy and childhood is also associated with childhood OB risk [7, 55, 242].

• Parental OW/OB: Having two Ob parents was related to greater weight gain from birth to 24 months independent of childhood appetitive traits [143]. Parental OW/OB was an independent risk factor for childhood OB, in particular, maternal BMI [153, 421]. • Socioeconomic status (SES): Numerous studies of both children

and adults indicate an inverse association between income or SES and BMI [3, 125]. Moreover, population studies in high-income countries show higher rates of OB in the lowest socioeconomic groups [328].

• Family history: longitudinal 30-year follow-up study showed that presence of diabetes in a family is an independent risk factor for children’s OW/OB [273].

• Ethnicity: some ethnic groups (Hispanic and South Asian) appear to have a tendency for OW [138].

3.4. Obesity-related metabolic complications

Despite the positive tendencies in OB rates, OB remains a major public health issue as it is well known to increase the risk of cardiovascular disea-ses (CVD), T2D, hyperlipidemia, and musculoskeletal disorders in adults [39, 275, 279].

Recent studies showed that childhood OW/OB may impact metabolic changes such as IR, prediabetes and T2D, lipid profile disorders,

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steatosis, ovarian hyperandrogenism, and proinflammatory state, related to excess of abdominal fat [67, 99, 152, 201]. Moreover, predisposed by OB proinflammatory state increases the risk of several chronic diseases inclu-ding asthma, obstructive sleep apnea (OSA) and several cancers in children and adults [23, 45, 126, 142, 145, 154, 155, 202, 236, 239, 313, 320, 361, 376, 395, 401, 416].

Several studies showed that Ob children and adolescents have significantly higher BP, IR assessed by homeostasis model assessment of IR (HOMA-IR), insulin, TG, LDL concentrations and lower HDL concentra-tions than do normal-weight (NW) children [158, 340].

3.4.1. Insulin resistance and impaired glucose metabolism

IR or reduced IS refers to an impaired function of insulin in mediating glucose uptake, transport, and storage [176]. IR with compensatory hyper-insulinemia is the initial step in T2D pathogenesis [167]. The subsequent step is impaired early insulin secretion, leading to post-prandial and later, fasting hyperglycemia, when clinical diabetes manifest [165, 166]. IR as well as fat mass and distribution were independently associated with metabolic risk [127], also is a key risk factor for CVD and T2D [127, 198].

Together with increase in pediatric OB, there has been a significant increase in the number of children and adolescents with clinical signs of IR. As there is no universally accepted biochemical definition of IR in children and adolescents, identification and diagnosis of IR usually relies on clinical features such as acanthosis nigricans, PCOS, AH, dyslipidaemia, and NAFLD [176].

Various techniques are available for assessing IS. The “gold standard” is euglycemic hyperinsulinemic clamp, which measures IS and beta-cell function directly [176]. However, this method is invasive, labor-intensive, and expensive, which makes it non-practical. Other methods are mostly based on glucose-insulin relationship and surrogate calculations based on blood samples. HOMA-IR is the most widely used surrogate of IS index in children and adolescent [223, 371]. However, IS index of Matsuda has been validates for the whole body IS evaluation, and is obtained from oral gluco-se tolerance test (OGTT), including both fasting and post-load glucogluco-se and insulin response [121, 371]. Despite ongoing clinical and scientific discus-sion, there is still no established definition for IR in children and adolescents [46, 222].

The prevalence of impaired glucose metabolism (IGM) among OW adults varies from 18.1% in United Kingdom [432] to 32% in Kuwait adults [8, 240]. The prevalence of IGM among OW/Ob children and adolescents

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ranges from 7.0% in Spanish [240, 244] to 12.6% in German populations [215].

3.4.2. Metabolic syndrome and changes in lipid profile

The cluster of symptoms defined as MS is known as a risk factor for the development T2D and CVD among children and adults [168]. Pediatric MS represents a cluster of risk factors associated with CVD, with features that include IR, OB, hyperlipidemia and AH [167]. MS is characterized by OB, assessed by increased WC, high TG and low HDL levels, increased BP and increased glucose level (fasting glucose or T2D). The International Diabetes Federatio (IDF) definition has been regarded as the most appropriate binary MS definition in children since it applies different criteria for each group, acknowledging that BP, lipid levels, and anthropometric variables change with age and pubertal development [253].

Abdominal OB that promotes IR is the most central factor underlying the MS in genetically predisposed individuals due to increased flux of free fatty acids, increased gluconeogenesis, and decreased insulin clearance by the liver [44]. In prediction models, the addition of family history of T2D or CVD to BMI almost double the predicted probability of adult MS (from 29% to 52%) [360].

The prevalence of MS varies in different populations according to age, gender, and ethnic origin and depends on the diagnostic criteria used [204]. According to the IDF criteria [441], the prevalence of MS among OW adults ranges from 25% to 43.8% [240].

According to the IDF diagnostic criteria in children and adolescents, the prevalence of MS varies from 1.9% in Brazilian to 15.9% in French OW children and adolescents [118, 391, 420]. The prevalence of MS in Ob children is reported to be considerably higher: from 3.7% in Chinese [225, 317, 391, 420] to 44.0% in US children and adolescents [91, 225, 317, 391, 420].

A former study by Luksiene et al. have found the prevalence of MS among 35-64 year old Lithuanian men and women according to IDF criteria to be 29.7% and 35.1%, respectively [246]. Recently published study showed that the prevalence of OW and OB among 7–17 years old children and adolescents in Lithuania is 12.6% and 4.1%, respectively [378]. To date, there are no data on metabolic disturbances in OW and Ob children and adolescents in Lithuania.

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3.4.3. Proinflammatory and cardiovascular status

Cardiovascular comorbidities include AH, dyslipidemia and risk for coronary heart disease. CVD is the leading cause of adult mortality and morbidity [167]. Bogalusa Heart Study found that while the NW children with central OB had adverse levels of cardiometabolic risk factor variables as compared to those without central OB, the OW/Ob without central OB had significantly lower levels in relation to those with central OB [270].

OB and IR promote release of free fatty acids and various adipokines from adipocytes, which lead to acute changes in vascular reactivity and chronic endothelial injury through inflammatory responses and oxidative stress [373].

In Danish Ob adolescents study, within the OB group insulin, LDL, and CRP were positively associated with BMI Z-scores. BP, IR, TCh, LDL, TG, CRP, interleukin-6 and tumor necrosis factor alpha were higher and HDL was lower in Ob adolescents compared to NW adolescents, whereas there were no differences between the groups for glucose and free fatty acids [158]. It has been documented that early stages of the atherosclerotic process are detectable in Ob children. Intima-media thickness (IMT) of the peripheral arterial vessels, a surrogate marker for atherosclerosis, has been found increased in Ob children and adolescents [263].

3.4.4. Nonalcoholic steatohepatitis (NASH) and nonalcoholic fatty liver disease (NAFLD)

NAFLD has become the most common cause of chronic liver disease in children in US. It encompasses a range of severity from bland steatosis to NASH, further resulting in advanced fibrosis, cirrhosis and hepatocellular carcinoma [167, 182, 232]. Pediatric NAFLD is associated with several fac-tors of MS, like central OB, dyslipidaemia (TG-emia and/or hyper-TCh-emia) and IR. Therefore, NAFLD can be considered as the hepatic manifestation of MS [12]. In adipocytes, bad dietary intake (i.e. elevated consumption of sweet high-fat foods) causes a specific pattern of lipid storage and metabolic stress, which in turn activate signaling cascades that induce oxidative stress and trigger an inflammatory response. Therefore, metabolic stressors and lipid partitioning may result in the release of several circulating adipocytokines (leptin, adiponectin, tumor necrosis factor α, interleukin 6, and resistin) with local action and systemic effects such as muscle and liver IR [14]. It has recently been demonstrated that Ob children with NAFLD display decreased levels of insulin-like growth factors IGF-1 and -2, which correlate with both IR and severity of NAFLD [81]. Positive association between NAFLD, thyroid dysfunction and MS in childhood was

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also evidenced. Thyroid stimulating hormone (TSH) was suggested as a potential predictor of liver, lipid and glucose metabolism disorders, indepen-dently of visceral OB [309]. WC and hypertriglyceridemia have been identi-fied as strong predictors of liver fibrosis and NAFLD [290]. Hyperinsuli-nemia, due to IR, most probably represents the first pathogenetic hit of NAFLD and may be a predictor for progressive hepatic fibrosis [356, 403].

Several publications demonstrated that children with NAFLD may pre-sent certain endothelial dysfunctions and greater carotid IMT, the latter considered a surrogate marker of early atherogenesis in childhood [129, 308, 310].

The diagnosis of liver fibrosis is based on liver biopsy, which is invasive and limited by hazard and discomfort to the patient [336, 403]. In clinical practice the diagnosis of NAFLD is usually suggested by finding elevated serum liver enzymes (mostly ALT and γ-glutamyl transpeptidase), and/or evidence of a bright liver on ultrasound, most frequently among OW/Ob children [294, 403]. However, abnormal serum aminotransferases in OW/Ob patients are not diagnostic of NAFLD/NASH. It is now widely accepted that the degree of ALT elevation doesn’t correlate with the presence or severity of histological findings of NAFLD. Serum ALT levels alone are a useful tool, but they are not adequate as a single marker for diagnosing NAFLD. A number of children with normal or minimal eleva-tion of ALT have advanced fibrosis on liver biopsy [403]. Ultrasonography is the most common imaging technique used for NAFLD screening due to it is safe, widely available and relatively inexpensive. The degree of fat infiltration is visually assessed by degree of echogenicity, but it does not let to distinguish between liver steatosis and fibrosis [403]. Other radiologic methods (unenhanced computed tomography, magnetic resonance imaging) are not used routinely due to its expensiveness. 1H-MR spectroscopy, fibroscan and magnetic resonance elastography are non-invasive, highly specific and sensitive, but not yet validated for daily clinical practice, espe-cially in children, and still are experimental [164]. Prevalence of NAFLD in children and adolescents is not clearly known, because a large part of children with NAFLD remain undiagnosed [14].

According to publications, the overall mean prevalence of NAFLD in general population is 7.6% (confidence interval (CI) 1.6–29.3%), meanwhile in OW/Ob pediatric population it varies from 27.1% to 45.0% with higher prevalence in males [19, 358, 440].

According to European Society for Pediatric Gastroenterology, Hepato-logy and Nutrition (ESPGHAN), the diagnostic algorithm for pediatric NAFLD includes abdominal ultrasound and liver function tests, followed by the exclusion of other liver diseases; high-risk children (OW/Ob) with

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mal ultrasound liver function tests should still be monitored; liver biopsy is recommended in cases of clinically suspected advanced liver disease [403]. However, no clear noninvasive diagnostic criteria are available for NAFLD are validated for children and adolescents. Two scores were suggested for pediatric NAFLD: the pediatric NAFLD fibrosis index (PNFI), based on anthropometrics (age and WC) and serum marker TG, and is able to predict liver fibrosis risk in children with NAFLD [290 889]. Another very new, but invasive, score is Pediatric NAFLD Histological Score (PNHS) with an excellent correlation to NASH presence, is the optimal choice for histolo-gical grading of NAFLD in children [13].

3.4.5. Polycystic ovarian syndrome

PCOS is a common endocrinopathy affecting woman’s fertility and increasing the risk of uterine bleeding and carcinoma [52]. The pathophysio-logy of PCOS appears to be a multigenic trait and still remains unclear [111]. Genetic and environmental factors for hormonal disturbances interact with other factors, including OB, ovarian dysfunction, and hypothalamic pituitary abnormalities to contribute to the etiology of PCOS [393]. In adults, OW and OB as well as hyperinsulinemia and T2D increase the risk of PCOS, which is often associated with IR [135]. On the other hand, PCOS itself, independently of OB or IR, increases the risk of T2D, AH, dyslipi-demia, and the MS. The overall risk of developing T2D and impaired glu-cose tolerance (IGT) in women with PCOS appears to be 3-to 7-fold higher compared with the healthy female population; glucose intolerance manifests at an earlier age and usually is associated with woman’s OW/OB [183]. OB is also associated with hyperandrogenism in women and adolescent girls, promoting acne vulgaris, hirsutism and androgenic alopecia.

PCOS in adolescents arises as a result of a genetically determined disor-der of ovarian function that results in clustering of hypersecretion of androgens [135] and/or insulin, which underpins PCOS [393].

The recent 3rd PCOS Consensus Workshop Group has stated that there is no agreement on the diagnosis of PCOS in adolescents [410]: the criteria of the National Institutes of Health (hyperandrogenism and evidence of anovulation as menstrual irregularity), the Rotterdam criteria (clinical/bio-chemical hyperandrogenism, oligo- and/or anovulation, and/or a polycystic ovary morphology (PCOM) on ultrasound), and the Androgen Excess and PCOS Society criteria (hyperandrogenism and ovarian dysfunction such as oligo-/anovulation and/or PCOM) are applied to diagnose PCOS in adoles-cent girls [345]. The signs and symptoms of PCOS typically appear at the onset of puberty [112]. However, the clinical and hormonal features of

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PCOS simulate the physiological features of puberty [111, 130] and are often wrongly considered as normal changes of adolescence. Physiological IR and hyperinsulinemia during puberty may be an underlying sequence of hormonal and metabolic aberrations in adolescents [111]. IS during puberty decreases by about 50% [345] followed by a compensatory increase in basal insulin levels at the end of puberty (Tanner stage 4-5), which are higher than in the prepubertal period or adulthood [111]. Insulin stimulates the synthesis of androgens in ovarian theca cells and inhibits the hepatic production of sex hormone-binding globulin (SHBG) [345]. A persistent and increased influence of insulin during puberty and adolescence may further worsen ovarian activity [410]. Moreover, IR stimulates the release of nonesterified fatty acids from the liver and adipose tissue due to a decreased activity of lipoprotein lipase, which contributes to PCOS-associated dyslipidemia [345].

The prevalence of PCOS in the general adolescent population varies from 0.56% to 1.14% [80] and is up to 38.9% among OW and Ob adoles-cent girls [208].

OB is a known risk factor associated with PCOS increasing the risk of the MS [441]. The prevalence of MS in adolescent girls with PCOS was shown to be up to 10.8%, which is 6 times higher as compared with girls without PCOS [345] and at least 3 times higher when adjusted for BMI [349].

18–25% of adolescents with PCOS may have abnormalities in glucose metabolism and IR [130, 183, 349]. Longitudinal studies involving adoles-cent girls have shown that the MS and abnormal metabolic risk factors persist from childhood to adulthood and predict adult CVD and T2D [345].

Lifestyle interventions are the first-line treatment for PCOS, especially when it is accompanied by OB. Only one intervention study was performed in Ob adolescent girls with PCOS; according to it results, have been shown that one-year lifestyle intervention is effective to lose weight, treat menses irregularities, and normalize androgens [230]. However, there is little infor-mation about intervention studies in Ob girls with PCOS.

3.4.6. Pulmonary comorbidities: asthma and obstructive sleep apnea

OSA is characterized by recurrent episodes of upper airway collapses during sleep. These recurrent episodes of upper airway collapse usually are accompanied by oxyhemoglobin desaturation and terminated by brief arou-sals which result in marked sleep fragmentation and chronic excessive daytime sleepiness [281].

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Many studies have shown that OSA is associated with increased cardio-vascular and cerebrocardio-vascular morbidity [18, 287, 319, 364]. Patients with OSA appear to have increased TCh, LDL, TG, and decreased HDL and is associated with OB and MS [66, 281]. Moreover, OSA caused hypercapnia is associated with suppression of hypothalamic gonadotropin-releasing hormone function and can lead to delayed puberty [203].

Ob children are up to six times more likely to have OSA than lean children. It is independently related to the development of AH, CVD, beha-vioral disorders and poor school performance [193]. Glucose and high sensi-tivity (hs)-CRP are significantly higher in OSA patients compared to those without OSA [23]. Moreover, OSA can lead to bone mass loss: chronic respiratory disease is associated with secondary osteoporosis as a conse-quence of hypogonadotrophic hypogonadism from one side, and OB-rela-ted, inflammation, oxidative stress, vitamin D (vit. D) deficiency and T2D on other side. Moreover, both OB and OSA influence sympathic nervous system and alter adrenergic tone – thus negatively impact bone metabolism by suppressing bone formation and promoting bone resorbtion [75].

The prevalence of asthma is also increased in Ob children [120, 132, 167, 197, 376]. Asthma is associated with abnormal lipid and glucose metabolism, IR and OB [4]. Interestingly, asthma stages were shown to be directly associated with serum leptin levels [285].

Compared to NW individuals, OW/Ob patients with asthma have poorer asthma control and respond less to corticosteroid therapy [229, 375]. Potential mechanism underlying OB and asthma worsening is unknown. OB affects lung function in adults with and without asthma because of changes in the elastic properties of the chest wall and lower tidal volumes, increased low-grade systemic inflammation and hyperresponsiveness of airway obstruction [321, 423].

Furthermore, weight loss in Ob adults with asthma can improve asthma severity, airway hyperresponsiveness, asthma control, and lung function [114, 311].

3.4.7. Other obesity-related complications

• Orthopedic complications include slipped capital femoral epiphy-sis, genu valga, tibia vara (Blount disease) and a higher incidence of fractures often without trauma [151]. OB is associated with increased rates of postoperative complications after various orthopedic procedures [40, 60, 295, 372, 424]. Morbidly Ob patients have significantly greater odds of wound dehiscence, deep wound infection, major complications, and total complications compared to patients of NW [426].

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• Dermatological complications can include skin manifestation (such as acanthosis nigricans, fibroma pedulans and striae distensae), promote skin infections like erysipelas and intertrigo, and worsen existing skin disease like psoriasis by releasing proinflammatory factors from adipose tissue [167, 374, 376]. Early onset OB is associated with cutaneous disorders characterized by hyperproliferation, inflammation, bacterial and fungal infection, and mechanical changes [269]. Acanthosis nigricans is an important cutaneous marker of IR.

• Neurological complications include idiopathic intracranial hypertension (IIH) also known as primary pseudotumor cerebri [26, 31]. Etiology that causes increased intracranial pressure without any identifiable cause is unknown, and reproductive-aged women are usually affected [20, 90], and in 30% to 90% of cases is associated with OW/OB [73, 146, 312]. Symptomatic includes headache, nausea, vomiting, transient vision loss, impaired visual fields, photopsias, diplopia, and eye pain. Ophthalmologic signs include decreased visual acuity, visual field loss, and papilledema [20, 106]. In publications, strong correlation between weight gain and IIH was observed [98, 350]. Moreover, positive correlation between BMI and serum leptin levels in individuals with IIH was recently reported [32, 228]. Weight-loss therapy as well as bariatric surgery were shown to be effective in reducing severity of IIH [169].

• Thyroid axis

Correlated with BMI, elevated TSH levels in association with normal or slightly elevated free thyroxine (FT4) and/or free triiodthyronine (FT3) levels have been found in Ob subjects, but the underlying mechanisms of these hormonal changes are still unclear [167, 218]. According to recent studies results, normal peripheral thyroid hormone levels suggested undisturbed peripheral metabolism in Ob children and moderate weight loss frequently restores these hormonal abnormalities [162, 241, 256].

Prevalence of isolated hyperthyreotropinemia is higher in Ob adoles-cents compared to NW counterparts, and varies from 9.3% to 23% among OW/Ob children and adolescents [35, 74, 338, 353]. Recent studies establi-shed positive correlation between TSH levels and IR, fasting insulin and leptin levels in OW individuals [16, 218, 397]. Study by Ambrosi et al. showed that Ob participants with IR had higher TSH and lower FT4 levels as compared to patients with normal IS [16]. Moreover, TSH significantly correlates with ALT, BMI-SDS, WC, TG and the degree of NAFLD in OW/Ob children and adolescents [96, 123, 307, 366, 402]. Such findings suggest that increased TSH level in OW/OB is a risk factor for MS, and rather a consequence of OB than a cause of it [306, 339, 353].

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• Bone metabolism

OB accelerates statural growth and causes advancement of the bone age [167]. Recent studies showed that body fat mass negatively correlates with vit. D levels and bone mineral density (BMD) [250, 274]. Despite lower serum vit. D and physical activity levels, BMD was found to be higher in adolescents with OB and associated with higher serum leptin concentrations [250]. Vit. D levels in Ob children with NAFLD are found to be signi-ficantly lower compared to Ob counterparts without NAFLD [190, 292]. According to Reinehr et al., bone turnover marker osteocalcin levels were found to be lower in Ob children and were related to IR and leptin [342].

3.4.8. Psychosocial complications

Psychosocial complications include body dissatisfaction, symptoms of depression, loss-of-control in eating, unhealthy and extreme weight control behaviors, impaired social relationship and stigma, bullying, also decreased health-related quality of life and poor self-esteem in OW/Ob children which are victimized by their peers and more anxious as well [65, 252, 407]. Buttitta et al. in their review publication highlighted that most dimensions of quality of life (QoL) are affected in OW and Ob children and adolescents: QoL score was lower in the OW and Ob groups compared with the NW groups, and impairment in QoL worsened with degree of OB [65].

3.5. Epigenetics and peroxisome proliferator activated

receptor-γ co-activator-1α (PPARGC1A) methylation

DNA methylation is a genomic modification that can influence gene activity, used in cells to lock genes in the “off” position (Fig. 3.5.1) [214, 326]. DNA methylation occurs at the cytosine bases of eukaryotic DNA, which are converted to 5-methylcytosine (5-mC) by DNA methyltransferase (DNMT) enzymes, which occurs almost exclusively in the context of paired symmetrical methylation of a CpG site, in which a cytosine nucleotide is located next to a guanidine nucleotide. Methylation is found sparsely but globally, distributed in definite CpG sequences throughout the entire geno-me, with the exception of CpG islands, or certain stretches (approximately 1 kilobase in length) where high CpG contents are found [326]. Methylation near gene promoters varies considerably depending on cell type, with more methylation of promoters correlating with low or no transcription [390].

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Fig. 3.5.1. DNA methylation regulating gene expression

A – CpG island promoter is unmethylated and allows binding of transcription factors,

which is required for transcription initiation. B – CpG island promoter methylation prevents binding of transcription factors and results in gene silencing.

A major candidate identified that undergoes epigenetic regulation and has been implicated in T2D is the master regulator of mitochondrial bioge-nesis PPARGC1A. PPARGC1A gene, located on 4p 15.2 chromosome, encodes homologous proteins that, through nuclear transcription factor coactivation, regulate adipogenesis, insulin signaling, lipolysis, mito-chondrial biogenesis, angiogenesis, hepatic gluconeogenesis, is linked to energy metabolism and homeostasis, and is activated by both dietary fatty acids and their metabolic derivates [134, 227]. Peroxisome proliferator activated receptor-γ (PPARγ) stimulates the formation of new adipose cells, therefore encouraging hyperplasia instead hypertrophy [171]. However, association with weight status is controversial: one study group found the positive association with BMI in Ob women [117], thereafter, other research group in their later study did not confirm the relation between weight status and PPARγ expression in adipose tissue [171].

Methylation of PPARGC1A promoter is critically involved in highly active metabolic tissue such as skeletal muscle and pancreas [381]. Recent finding had shown that “mitochondrial epigenetics” such as mitochondrial DNA (mtDNA) may be a subject to methylation by an isoform of DNA DNMT1, which is upregulated by PPARGC1A [368]. There is a close rela-tion among metabolic stressors, mitochondrial biogenesis, and mtDNA copy number. Study by Sookoian et al. found that mitochondrial biogenesis is reduced in the liver of NAFLD patients, and this reduction was inversely

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associated with peripheral IR, fasting glucose also insulin levels, and PPARGC1A promoter methylation status [381, 382]. Reduced gene expres-sion of PPARGC1A has been detected in skeletal muscles from both T2D patients and non-diabetic first degree relatives with IR [34, 68, 156, 318].

Epigenetic studies have also suggested that the expression of PPARGC1A may be controlled at the level of DNA methylation of PPARGC1A promoter, at both cytosine-guanine dinucleotide (CpG) sites and non-CpG sites. The increase in DNA methylation of PPARGC1A promoter appears to be a shared mechanism by multiple tissues in patients with T2DM or NAFLD, which reduces PPARGC1A expression and mitochondrial content, resulting in impair-red insulin secretion from pancreatic islets and impair-reduced IS in the liver [83].

So far, the effect of weight change and treatment with insulin sensitizers have not being studied. The aim of this part of the study is to evaluate intervention-related changes in PPARGC1A methylation, as well as to assess anthropometric, biochemical and hormonal parameters dynamics in OW/Ob children and adolescents in relation to changes in PPARGC1A methylation.

3.6. Obesity treatment strategy

OW/OB treatment strategies in children and adolescents are very limited.

Lifestyle modification involving nutrition and physical activity

re-mains the main treatment approach in childhood OB. According to previous studies, behavioral interventions that target dietary changes and increased physical activity have a heterogeneous picture and may lead to favourable changes in body composition in Ob youth. Intensive dietary interventions without adjunct exercise therapy elicit a ~2% weight loss defined as weight, BMI, percentage of body fat, WC or SFT relative to controls, receiving standard dietary recommendations [161, 174, 180, 262, 305, 427]. Various dietary strategies found to be effective in improving body composition in the short term among OW and Ob pediatric patients, particularly young children. However, all dietary strategies appeared to yield similar effects on adiposity measures and cardiometabolic risks in Ob children and adoles-cents [262].

Supervised intensive lifestyle interventions that include both diet and physical activity components tend to yield greater weight loss compared with standard weight loss recommendations. Moreover, the effects of these interventions on BMI and weight loss were greater in children under the age of 12 years than in adolescents aged 13–19 years [175]. In particular,

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children aged 5-12 years with OW rather than OB profit more from lifestyle interventions. However, in clinical practice, the degree of weight loss with lifestyle intervention is only moderate, and the success rate 2 years after onset of an intervention is low (<10% with a decrease in BMI-SDS of <0.25). Nevertheless, the difficulty of a child with OW/OB to reduce their weight might be attributable to not only a lack of motivation but also genetic background and/or adaptive changes in basal metabolic rate, hunger and satiety hormones that occur with weight loss [337], also long-term stabili-zation of reduced weight is questionable taking into account high drop out and the frequent relapse of OB in these patients [405].

Pharmacotherapy options for the treatment of pediatric OB are very limited. Therefore, it is crucial to establish a comprehensive management program that emphasizes appropriate nutrition, exercise and behavior modi-fication.

Treatment with Orlistat and Sibutramine have been shown having weight reducing effect in OW/Ob children and adolescents, but the rate of adverse effects was significant; therefore, nowadays they are not recommended for use in pediatric patients in Europe [257].

Recent studies showed positive impact of Metformin on weight and IR in insulin-resistant OW/Ob children and adolescents. Metformin acts by induction of hepatic gluconeogenesis inhibition: reduced hepatic uptake of substrates for gluconeogenesis, increased phosphorylation of insulin receptor and insulin receptor substrates-1 and -2, stimulates glucose entry into the liver and glycolysis through the activation of glycolytic enzymes such as glucokinase and pyruvate kinase [113]. Moreover, Metformin treatment improves skeletal muscle IS, directly inhibits adipogenesis, modulates synthesis and secretion of adipokines in adipose tissue, improves endothelium-dependent vasodilatation in IR-patients thus protecting from atherosclerosis, and directly stimulates several steroidogenic enzymes in the ovary which suppress androgen production by insulin-independent action [53, 113, 277].

Clinical trials with Metformin use in adults with non-diabetic status report contrary results, some showing beneficial effect in OB reduction, lipid profile and IS improvement, androgen excess reduction and ovulation restoration in women [11, 113], while others reporting no significant diffe-rences compared to non-Metformin users [232].

Metformin in pediatric age is registered for T2D treatment only. Data on Metformin use in non-diabetic children and adolescents are scars, but clinical trials partially confirm beneficial effect of Metformin in OW/Ob children and adolescents similarly to that in adults. The main indications for Metformin use in pediatric age non-diabetic subjects are OB, IR and PCOS.

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Metformin has been shown to have a moderate effect on BMI reduce in Ob adolescents with hyperinsulinemia. First time Metformin for OB treatment in children was used by Lutjens et al. in 1977: results of 9 OB children aged 8–14 years treatment with Metformin for 3 months showed a significant reduce in insulin levels, insulinogenic indexes and weight [248].

First randomized double-blind placebo-controlled clinical trial with Metformin was performed by Freemark et al. in 2001, where 29 morbidly Ob 12–19 years old adolescents were treated with Metformin 1000 mg daily for 6 months. Significant weight decrease was registered in Metformin group, however IS left unchanged [141].

Later studies results of different dosage (500–2000 mg/d) short-term use (up to 6 months) as well as long-term (over 6 months) of Metformin in OW/Ob children and adolescents showed good tolerance of Metformin and beneficial effect on both BMI, WC and IS [25, 57, 84, 189, 212, 384, 433].

Several studies of Metformin treatment in girls with PCOS showed beneficial effects in terms of restored regular menses, decreased hirsutism score and androgen levels, as well as induced ovulation [157, 185, 186]. Also, positive effect of Metformin on NAFLD status was reported [362].

However, some studies did not find significant impact of Metformin on weight [245, 429] or NAFLD [232, 293]. Moreover, data on pharmacoki-netics and long-term efficacy and safety are lacking as well as evidence-based dosing regimen for this age group [405].

Considering lack of consensus regarding the most effective strategy in weight regulation in children and adolescents, the intervention part of this study was based on combination of various types of intervention, such as lifestyle changes and pharmaceutical, to evaluate the most effective strategy for weight and OB-related risks reduction.

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4. METHODS

4.1. Study design

4.1.1. School survey 4.1.1.1. Material

Lithuanian Bioethics Committee approved the study (Nr. BE-2-1, 2009-01-22). Written informed consent was obtained from the parents of all study participants.

Sample size (n) for school survey was calculated using formula n = Zp2 × p × (1–p) / Δ2, where Zp – normal variety (1.96); p – expected proportion of OW/OB (25% according to the literature); Δ – absolute error (0.02) [282]. Calculated sample size is 1801.

This cross-sectional survey included 2,213 10–17 years old school-children from 17 schools of Kaunas region, which were randomly selected from the institutional registry list of the Ministry of Education and Science. The calculated population-based sample was 1,801 schoolchildren; the response rate was 123.0%. The study was conducted from October 2008 to February 2010.

This survey results were were published by Smetanina et al. “Preva-lence of overweight/obesity in relation to dietary habits and lifestyle among 7–17 years old children and adolescents in Lithuania” (see Publications, p. 168) that included data on of two different pediatric age groups: 7–9 years old schoolchildren survey was performed by Petrauskiene et al., and 10–17 years old schoolchildren were evaluated by Smetanina et al. Study question-naire was adopted for respondents’ age: questionquestion-naire of 7–9 years old children was filled in by their parents, and 10–17 years old children and adolescents filled it by themselves. From the 10–17 years old cohort, child-ren and adolescents with OW/OB were further invited for metabolic eva-luation and intervention study.

4.1.1.2. Investigation of the schoolchildren

The measurements were performed by standardized equipment. Height was measured to the nearest 0.1 cm with a portable SECA stadiometer (Seca®214). Weight was measured to the nearest 0.1 kg using portable SECA electronic scales (Seca®813). Body mass index (BMI) was calcula-ted by using the standard equation: BMI = weight (kg) / height (m2). WC, hip, middle thigh and middle arm on the left side were measured by non-elastic type to the nearest 0.1 cm.

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The BMI category (underweight (UW), NW, OW, and OB) was defined using the IOTF BMI cut-offs according to age and gender [87–89]. LSM Chart maker software version 2.54 (Medical research Council, UK) was applied for anthropometric parameters percentiles creation.

Data of study participants’ dietary habits, physical inactivity and family socioeconomic status (SES) were collected from the study questionnaire, which was formed of modified WHO questionnaires (conducted by Health Behaviour in School-aged Children (HBSC) and COSI study groups) [95, 409]. The study questionnaire included 10 questions on food intake items (food frequency questionnaire (FFQ) part: breakfast eating frequency per week and meal frequency per day, consumption of different meals (fresh fruits and vegetables, sweets, sweetened beverages, energy-dense fast food) frequency per week), 4 questions on screen time (TV and personal computer (PC) use) on weekdays and weekends (how many hours do respondents spend daily), 5 questions on family SES (parental marital status (live in single or two parents’ family), parents’ education level, employment and type of their job), and 3 questions of psychosocial factors (health status self-evaluation, QoL satisfaction, and self-esteem, pointed 1-10). School performance assessment was done by analysis of the school year graduation evaluation.

All study children/adolescents were asked to fill-in the questionnaire by themselves at school. Anthropometric measurements of study participants were done at school.

For further analysis, schoolchildren were grouped into 3 age groups: 1) 10 to 12 (10.0–12.99) years old;

2) 13 to 14 (13.0–14.99) years old; 3) 15 to 17 (15.0–17.99) years old.

According to IOTF cut-off for BMI in children and adolescents, study participants were divided into 4 groups:

• UW (BMI-SDS ≤ –1.0 according to age and gender); • NW (BMI-SDS from –0.99 to 0.99);

• OW (BMI-SDS from 1.0 to 1.99); • OB (BMI-SDS ≥ 2.0)

The participants were asked how many times per day they had their meal. The answers were grouped into 3 categories:

1) “3 or fewer times per day”; 2) “4–5 times per day”;

3) “6 and more times per day”. 35

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The answers of the question “How often do you have your breakfast” were grouped into 4 categories as follows:

1) “Everyday”

2) “4–5 times per week”; 3) “1–3 times per week” 4) “Never”.

Different type food preferences were evaluated if the respondent consu-med it 0–3 or more than 4 days per week.

Physical activity was evaluated by questions about watching TV, time spent by the computer during working days and if did they spend more time with the same activity during weekend. The answers to the questions about TV and PC use were grouped as follows:

1) “never”;

2) “1–2 hours per day”;

3) “3 and more hours per day”.

Physical activity was grouped according hours per week spent in sports: 1) “Never or 2 hours per week”;

2) “3–4 hours per week”; 3) “5–7 hours per week”.

4.1.2. Overweight and obese children and adolescents study 4.1.2.1. Material

Study was registered on Clinicaltrials.gov and EudraCT (EudraCT No. 2011-006352-36). All children and their parents signed an informed consent form approved by the Lithuanian Bioethics Committee (BE-2-1, 2009-01-22; P-13-24, 2013-03-22). State Medicine Control Agency of Li-thuania also gave their permission for the study (Nr. 12KL-68, 2013-04-11).

Inclusion criteria:

• Age 10–17 years old;

• BMI ≥ 1.0 SDS by gender according to IOTF criteria; • Lives in Kaunas district;

• Absence of any chronic, endocrine or genetic diseases which might influence weight and metabolism (i.e. Prader-Willi syndrome, Turner syndrome, renal insufficiency, severe form of bronchial asthma, hypothyroidism, diabetes, insulin treatment);

• No long-term (> 1 month) treatment with glucocorticoids orally or intravenously (exception- intranasal or inhaler);

• Non-pregnant and non-planning to become pregnant; 36

OVERWEIGHT AND OBESITY IN CHILDREN AND ADOLESCENTS: ETIOLOGY, COMPLICATIONS AND EFFECTS OF 12-MONTHS INTERVENTION

Natalija Smetanina